Professor Fang Chen is a prominent leader in AI/data science with international reputation and industrial recognition. She is the winner the 'Oscars' of Australian science, 2018 Australian Museum Eureka Prize for Excellence in Data Science.
She has created many innovative research and solutions, transforming industries that utilise AI/data science. She has helped industries worldwide advance towards excellence in increasing their productivity, innovation, profitability, and customer satisfaction. The transformations to industry with practical impact won her many industrial recognitions including being named as “Water Professional of The Year” in 2016.
She has actively led in developing new strategies, which prioritise the organisation’s objectives, and capitalise on any growth opportunities. She has built up a career in creating research and business plans, and executing with leadership and passion.
In science and engineering, Professor Chen has 300+ refereed publications, including several books. She has filed 30+ patents in Australia, US, Canada, Europe, Japan, Korea, Mexico and China.
Can supervise: YES
This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning.
This book explores robust multimodal cognitive load measurement with physiological and behavioural modalities, which involve the eye, Galvanic Skin Response, speech, language, pen input, mouse movement and multimodality fusions.
Zhou, J & Chen, F 2018, 'DecisionMind: revealing human cognition states in data analytics-driven decision making with a multimodal interface', Journal on Multimodal User Interfaces, vol. 12, no. 2, pp. 67-76.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer International Publishing AG. Despite the recognized value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, significant barriers to widespread adoption and local implementation of ML approaches still exist in the areas of trust (of ML results), comprehension (of ML processes) and related workload, as well as confidence (in decision making based on ML results) by users. This paper argues that the revealing of human cognition states with a multimodal interface during ML-based data analytics-driven decision making could provide a rich view for both ML researchers and domain experts to learn the effectiveness of ML technologies in applications. On the one hand, human cognition states could help understand to what degree users accept innovative technologies. On the other hand, through understanding human cognition states during data analytics-driven decision making, ML-based decision attributes and even ML models can be adaptively refined in order to make ML transparent. The paper also identifies examples of impact challenges and obstacles, as well as high-demand research directions in making ML transparent.
Zhou, J, Arshad, SZ, Wang, X, Li, Z, Feng, D & Chen, F 2018, 'End-User Development for Interactive Data Analytics: Uncertainty, Correlation and User Confidence', IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, vol. 9, no. 3, pp. 383-395.View/Download from: Publisher's site
Ebrahimi, M, ShafieiBavani, E, Wong, R & Chen, F 2018, 'Twitter user geolocation by filtering of highly mentioned users', JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, vol. 69, no. 7, pp. 879-889.View/Download from: UTS OPUS or Publisher's site
Wen, T, Cai, C, Gardner, L, Dixit, V, Waller, ST & Chen, F 2018, 'A Strategic User Equilibrium for Independently Distributed Origin-Destination Demands', COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, vol. 33, no. 4, pp. 316-332.View/Download from: UTS OPUS or Publisher's site
Wen, T, Gardner, L, Dixit, V, Waller, ST, Cai, C & Chen, F 2018, 'Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network', Computer-Aided Civil and Infrastructure Engineering, vol. 33, no. 4, pp. 282-299.View/Download from: UTS OPUS or Publisher's site
Wen, T, Mihăiţă, A-S, Nguyen, H, Cai, C & Chen, F 2018, 'Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models', Transportation Research Record, vol. 2672, pp. 247-256.View/Download from: UTS OPUS or Publisher's site
This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2018, 'High-Quality Image Captioning with Fine-Grained and Semantic-Guided Visual Attention', IEEE Transactions on Multimedia.View/Download from: UTS OPUS or Publisher's site
IEEE The soft-attention mechanism is regarded as one of the representative methods for image captioning. Based on the end-to-end Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) framework, the soft-attention mechanism attempts to link the semantic representation in text (i.e., captioning) with relevant visual information in the image for the first time. Motivated by this approach, several state-of-the-art attention methods are proposed. However, due to the constraints of CNN architecture, the given image is only segmented to the fixed-resolution grid at a coarse level. The visual feature extracted from each grid indiscriminately fuses all inside objects and/or their portions. There is no semantic link between grid cells. In addition, the large area "stuff" (e.g., the sky or a beach) cannot be represented using the current methods. To address these problems, this paper proposes a new model based on the Fully Convolutional Network (FCN)-LSTM framework, which can generate an attention map at a fine-grained grid-wise resolution. Moreover, the visual feature of each grid cell is contributed only by the principal object. By adopting the grid-wise labels (i.e., semantic segmentation), the visual representations of different grid cells are correlated to each other. With the ability to attend to large area "stuff", our method can further summarize an additional semantic context from semantic labels. This method can provide comprehensive context information to the language LSTM decoder. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can accurately link the relevant visual information with each semantic meaning inside the text. Demonstrated by three experiments including both qualitative and quantitative analyses, our model can generate captions of high quality, specifically high levels of accuracy, completeness, and diversity. Moreover, our model significantly outperforms all other methods that use VGG-based ...
Zhou, J, Sun, J, Wang, Y & Chen, F 2017, 'Wrapping practical problems into a machine learning framework: Using water pipe failure prediction as a case study', International Journal of Intelligent Systems Technologies and Applications, vol. 16, no. 3, pp. 191-207.View/Download from: Publisher's site
Copyright © 2017 Inderscience Enterprises Ltd. Despite the recognised value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practice because of complicated interfaces between ML algorithms and users. This paper presents a work flow of wrapping practical problems into an ML framework. The water pipe failure prediction is used as a case study to show that the applying process can be divided into various steps: obtain domain data, interview with domain experts, clean/pre-process and preview original domain data, extract ML features, set up ML models, explain ML results and make decisions, as well as make feedback to the system based on decision making. In this process, domain experts and ML developers need to collaborate closely in order to make this workflow more effective.
Wei, W, Zhou, J, Chen, F & Yuan, H 2016, 'Constrained differential evolution using generalized opposition-based learning', SOFT COMPUTING, vol. 20, no. 11, pp. 4413-4437.View/Download from: Publisher's site
Zhou, J, Wang, X, Cui, H, Gong, P, Miao, X, Miao, Y, Xiao, C, Chen, F & Feng, D 2016, 'Topology-aware illumination design for volume rendering', BMC BIOINFORMATICS, vol. 17.View/Download from: UTS OPUS or Publisher's site
Liu, Y, Wang, Y, Sowmya, A & Chen, F 2016, 'Soft Hough Forest-ERTs:. Generalized Hough Transform based object detection from soft-labelled training data', PATTERN RECOGNITION, vol. 60, pp. 145-156.View/Download from: Publisher's site
Diez, A, Nguyen, LDK, Alamdari, MM, Wang, Y, Chen, F & Runcie, P 2016, 'A clustering approach for structural health monitoring on bridges', JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING, vol. 6, no. 3, pp. 429-445.View/Download from: UTS OPUS or Publisher's site
Copyright © 2015 Inderscience Enterprises Ltd. Despite the recognised value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practise because of complicated interfaces between ML algorithms and users. This paper focuses on investigating making ML useable from the point of view of how human-computer interaction (HCI) techniques benefit ML in order to simplify the interface between users and ML algorithms. We formulate possible research directions in making ML useable based on human factors, decision making and trust in ML. We strongly believe that a trustworthy decision making based on ML results, which is the ultimate goal of ML-based applications, contributes to the overall application performance and makes ML more useable. Two case studies of measurable decision making and revealing internal states of ML process are presented to show how HCI techniques are used to make ML useable.
Zhou, J, Sun, J, Chen, F, Wang, Y, Taib, R, Khawaji, A & Li, Z 2015, 'Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface', ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION, vol. 21, no. 6.View/Download from: Publisher's site
Hussain, MS, Calvo, RA & Chen, F 2014, 'Automatic Cognitive Load Detection from Face, Physiology, Task Performance and Fusion During Affective Interference', INTERACTING WITH COMPUTERS, vol. 26, no. 3, pp. 256-268.View/Download from: Publisher's site
Khawaja, MA, Chen, F & Marcus, N 2014, 'Measuring Cognitive Load Using Linguistic Features: Implications for Usability Evaluation and Adaptive Interaction Design', INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, vol. 30, no. 5, pp. 343-368.View/Download from: Publisher's site
Li, Z, Zhang, B, Wang, Y, Chen, F, Taib, R, Whiffin, V & Wang, Y 2014, 'Water pipe condition assessment: a hierarchical beta process approach for sparse incident data', MACHINE LEARNING, vol. 95, no. 1, pp. 11-26.View/Download from: Publisher's site
Zhang, B, Wang, Y & Chen, F 2014, 'Multilabel Image Classification via High-Order Label Correlation Driven Active Learning', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 23, no. 3.View/Download from: Publisher's site
Herman, G, Zhang, B, Wang, Y, Ye, G & Chen, F 2013, 'Mutual information-based method for selecting informative feature sets', Pattern Recognition, vol. 46, no. 12, pp. 3315-3327.View/Download from: Publisher's site
Feature selection is one of the fundamental problems in pattern recognition and data mining. A popular and effective approach to feature selection is based on information theory, namely the mutual information of features and class variable. In this paper we compare eight different mutual information-based feature selection methods. Based on the analysis of the comparison results, we propose a new mutual information-based feature selection method. By taking into account both the class-dependent and class-independent correlation among features, the proposed method selects a less redundant and more informative set of features. The advantage of the proposed method over other methods is demonstrated by the results of experiments on UCI datasets (Asuncion and Newman, 2010 ) and object recognition. © 2013 Elsevier Ltd.
Zarjam, P, Epps, J, Chen, F & Lovell, NH 2013, 'Estimating cognitive workload using wavelet entropy-based features during an arithmetic task', COMPUTERS IN BIOLOGY AND MEDICINE, vol. 43, no. 12, pp. 2186-2195.View/Download from: Publisher's site
Zhang, B, Guo, T, Zhang, L, Lin, P, Wang, Y, Zhou, J & Chen, F 2018, 'Water pipe failure prediction: A machine learning approach enhanced by domain knowledge' in Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, Springer, Switzerland, pp. 363-383.View/Download from: UTS OPUS or Publisher's site
Zhou, J & Chen, F 2018, '2D Transparency Space—Bring Domain Users and Machine Learning Experts Together' in Human and Machine Learning, Springer, Germany, pp. 3-19.View/Download from: UTS OPUS or Publisher's site
Machine Learning (ML) is currently facing prolonged challenges with the user acceptance of delivered solutions as well as seeing system misuse, disuse, or even failure. These fundamental challenges can be attributed to the nature of the 'black-box' of ML methods for domain users when offering ML-based solutions. That is, transparency of ML is essential for domain users to trust and use ML confidently in their practices. This chapter argues for a change in how we view the relationship between human and machine learning to translate ML results into impact. We present a two-dimensional transparency space which integrates domain users and ML experts together to make ML transparent. We identify typical Transparent ML (TML) challenges and discuss key obstacles to TML, which aim to inspire active discussions of making ML transparent with a systematic view in this timely field.
Zhou, J, Yu, K & Chen, F 2018, 'Revealing User Confidence in Machine Learning-Based Decision Making' in Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, Springer, Switzerland, pp. 225-244.View/Download from: UTS OPUS or Publisher's site
Chen, F, Zhou, J & Yu, K 2017, 'Multimodal and data-driven cognitive load measurement' in Cognitive Load Measurement and Application: A Theoretical Framework for Meaningful Research and Practice, pp. 147-178.View/Download from: Publisher's site
© 2018 Taylor & Francis. Cognitive load (CL), or mental workload, is an important issue in various application areas such as human-computer interaction (HCI), adaptive automation and training, traffic control, performance prediction, driving safety, and military command and control (Byrne & Parasuraman, 1996; Coyne, Baldwin, Cole, Sibley, & Roberts, 2009; Grootjen, Neerincx, Weert, & Truong, 2007). Many definitions exist for cognitive load, and one of the most widely accepted definitions is that it is a multidimensional construct representing the load imposed on the working memory during performance of a cognitive task (Paas & van Merriënboer, 1994; Paas, Tuovinen, Tabbers, & Van Gerven, 2003). The term working memory has been extensively used since its first appearance in the classic work on information processing capacity, which to some extent quantifies the capability of the human brain (Baddeley, 1986; Baddeley, Thomson, & Buchanan, 1975). The concept of working memory set the foundation for the cognitive load analysis thereafter and, to many researchers, cognitive load research is the examination of how and to what extent the working memory is deployed and utilized during a specific cognitive task.
Ebrahimi, M, ShafieiBavani, E, Wong, R & Chen, F 2017, 'Exploring celebrities on inferring user geolocation in twitter' in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 395-406.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. Location information of social media users provides crucial context to monitor real-time events such as natural disasters, terrorism and epidemics. Since only a small amount of social media data are geotagged, inference techniques play a substantial role to predict user spatial locations by incorporating characteristics of their behavior. Based on utilized source of information, related works are divided into text-based (based on text posted by users), network-based (based on the friendship network), and some hybrid methods. In this paper, we propose a novel approach based on the notion of celebrities to infer the location of Twitter users. We categorize highly-mentioned users (celebrities) into local and global, and consequently utilize local celebrities as a major location indicator for inference. A label propagation algorithm is then utilized over a refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments using three standard Twitter benchmark datasets demonstrate the superior performance of our approach over the state-of-the-art methods.
Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 2019, 'Multitask learning for sparse failure prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 3-14.View/Download from: Publisher's site
© Springer Nature Switzerland AG 2019. Sparsity is a problem which occurs inherently in many real-world datasets. Sparsity induces an imbalance in data, which has an adverse effect on machine learning and hence reducing the predictability. Previously, strong assumptions were made by domain experts on the model parameters by using their experience to overcome sparsity, albeit assumptions are subjective. Differently, we propose a multi-task learning solution which is able to automatically learn model parameters from a common latent structure of the data from related domains. Despite related, datasets commonly have overlapped but dissimilar feature spaces and therefore cannot simply be combined into a single dataset. Our proposed model, namely hierarchical Dirichlet process mixture of hierarchical beta process (HDP-HBP), learns tasks with a common model parameter for the failure prediction model using hierarchical Dirichlet process. Our model uses recorded failure history to make failure predictions on a water supply network. Multi-task learning is used to gain additional information from the failure records of water supply networks managed by other utility companies to improve prediction in one network. We achieve superior accuracy for sparse predictions compared to previous state-of-the-art models and have demonstrated the capability to be used in risk management to proactively repair critical infrastructure.
Yu, K, Berkovsky, S, Taib, R, Zhou, J & Chen, F 2019, 'Do I trust my machine teammate?: an investigation from perception to decision', Proceedings of the 24th International Conference on Intelligent User Interfaces, ACM, pp. 460-468.View/Download from: UTS OPUS
Zhou, J, Li, Z, Yu, K, Chen, F, Wang, Y, Hu, H & Li, Z 2019, 'Effects of influence on user trust in predictive decision making', Conference on Human Factors in Computing Systems - Proceedings.View/Download from: UTS OPUS or Publisher's site
© 2019 Copyright held by the owner/author(s). This paper introduces fact-checking into Machine Learning (ML) explanation by referring training data points as facts to users to boost user trust. We aim to investigate what influence of training data points, and how they affect user trust in order to enhance ML explanation and boost user trust. We tackle this question by allowing users check the training data points that have the higher influence and the lower influence on the prediction. A user study found that the presentation of influences significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts.
Liang, B, Li, Z, Wang, Y & Chen, F 2018, 'Long-Term RNN: Predicting Hazard Function for Proactive Maintenance of Water Mains', CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 27th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, Torino, ITALY, pp. 1687-1690.View/Download from: Publisher's site
Wang, W, Xu, J, Wang, Y, Cai, C & Chen, F 2018, 'DualBoost : Handling Missing Values with Feature Weights and Weak Classifiers that Abstain', CIKM'18 Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, Inc., Italy, pp. 1543-1546.View/Download from: UTS OPUS or Publisher's site
Zhang, B, Zhang, L, Guo, T, Wang, Y & Chen, F 2018, 'Simultaneous Urban Region Function Discovery and Popularity Estimation via an Infinite Urbanization Process Model', KDD'18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM SIGKDD International Conference on Knowledge Discovery and Data Minin), Association for Computing Machinery, London, ENGLAND, pp. 2692-2700.View/Download from: UTS OPUS or Publisher's site
Zhang, J, Li, B, Fan, X, Wang, Y & Chen, F 2018, 'Corrosion prediction on sewer networks with sparse monitoring sites: A case study', Advances in Knowledge Discovery and Data Mining 22nd Pacific-Asia Conference, PAKDD 2018 Melbourne, VIC, Australia, June 3–6, 2018 Proceedings, PartI (LNAI 10937), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Australia, pp. 223-235.View/Download from: UTS OPUS or Publisher's site
© Springer International Publishing AG, part of Springer Nature 2018. Sewer corrosion is a widespread and costly issue for water utilities. Knowing the corrosion status of a sewer network could help the water utility to improve efficiency and save costs in sewer pipe maintenance and rehabilitation. However, inspecting the corrosion status of all sewer pipes is impractical. To prioritize sewer pipes in terms of corrosion risk, the water utility requires a corrosion prediction model built on influential factors that cause sewer corrosion, such as hydrogen sulphide (H 2 S) and temperature. Unfortunately, monitoring sites of influential factors are very sparse on the sewer network such that a reliable prediction has often been hampered by insufficient observations – It is a challenge to predict H 2 S distribution and sewer corrosion levels on the entire sewer network with a limited number of monitoring sites. This work leverages a Bayesian nonparametric method, Gaussian Process, to integrate the physical model developed by domain experts, the sparse H 2 S and temperature monitored records, and the sewer geometry to predict corrosion risk levels on the entire sewer network. A case study has been conducted on a real data set of a water utility in Australia. The evaluation results well demonstrate the effectiveness of the model and admit promising applications for water utilities, including prioritizing high corrosion areas and recommending chemical dosing profiles.
Zhang, Z, Wang, L, Wang, Y, Zhou, L, Zhang, J & Chen, F 2018, 'Instance Image Retrieval by Aggregating Sample-based Discriminative Characteristics', ICMR '18 Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, ACM International Conference on Multimedia Retrieval, ASSOC COMPUTING MACHINERY, Yokohama, Japan, pp. 91-99.View/Download from: Publisher's site
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2018, 'Fine-grained and semantic-guided visual attention for image captioning', Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, Winter Conference on Applications of Computer Vision, IEEE, Lake Tahoe, NV, USA, pp. 1709-1717.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. Soft-attention is regarded as one of the representative methods for image captioning. Based on the end-to-end CNN-LSTM framework, it tries to link the relevant visual information on the image with the semantic representation in the text (i.e. captioning) for the first time. In recent years, there are several state-of-the-art methods published, which are motivated by this approach and include more elegant fine-tune operation. However, due to the constraints of CNN architecture, the given image is only segmented to fixed-resolution grid at a coarse level. The overall visual feature created for each grid cell indiscriminately fuses all inside objects and/or their portions. There is no semantic link among grid cells, although an object may be segmented into different grid cells. In addition, the large-area stuff (e.g. sky and beach) cannot be represented in the current methods. To tackle the problems above, this paper proposes a new model based on the FCN-LSTM framework which can segment the input image into a fine-grained grid. Moreover, the visual feature representing each grid cell is contributed only by the principal object or its portion in the corresponding cell. By adopting the pixel-wise labels (i.e. semantic segmentation), the visual representations of different grid cells are correlated to each other. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can better link the relevant visual information with each semantic meaning inside the text through LSTM. Without using the elegant fine-tune, the comprehensive experiments show promising performance consistently across different evaluation metrics.
Chen, Z, Zhou, J, Wang, X, Swanson, J, Chen, F & Feng, D 2017, 'Neural Net-Based and Safety-Oriented Visual Analytics for Time-Spatial Data', 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), International Joint Conference on Neural Networks (IJCNN), IEEE, Anchorage, AK, pp. 1133-1140.
Luo, S, Chu, VW, Zhou, J, Chen, F, Wong, RK & Huang, W 2017, 'A Multivariate Clustering Approach for Infrastructure Failure Predictions', 2017 IEEE 6TH INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS 2017), IEEE 6th International Congress on Big Data (BigData Congress), IEEE, Honolulu, HI, pp. 274-281.View/Download from: Publisher's site
Luo, S, Duh, HBL, Zhou, J & Chen, F 2017, 'BVP signal feature analysis for intelligent user interface', Conference on Human Factors in Computing Systems - Proceedings, pp. 1861-1868.View/Download from: Publisher's site
Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM). The Blood Volume Pulse (BVP) sensor has been becoming increasingly common in devices such as smart phones and smart watches. These devices often use BVP to monitor the heart rate of an individual. There has been a large amount of research linking the mental and emotional changes with the physiological changes. The BVP sensor measures one of these physiological changes known as Heart Rate Variability (HRV). HRV is known to be closely related to Respiratory Sinus Arrhythmia (RSA) which can be used as a measurement to quantify the activity of the parasympathetic activity. However, the BVP sensor is highly susceptible to noise and therefore BVP signals often contain a large number of artefacts which make it difficult to extract meaningful features from the BVP signals. This paper proposes a new algorithm to filter artefacts from BVP signals. The algorithm is comprised of two stages. The first stage is to detect the corrupt signal using a Short Term Fourier Transform (STFT). The second stage uses Lomb-Scargle Periodogram (LSP) to approximate the Power Spectral Density (PSD) of the BVP signal. The algorithm has shown to be effective in removing artefacts which disrupt the signal for a short period of time. This algorithm provides the capability for BVP signals to be analysed for frequency based features in HRV which traditionally could be done from the cleaner signals from electrocardiogram (ECG) in medical applications.
Yu, K, Conway, D, Berkovsky, S, Zhou, J, Taib, R & Chen, F 2017, 'User Trust Dynamics: An Investigation Driven by Differences in System Performance', IUI'17: PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, 22nd International Conference on Intelligent User Interfaces (IUI), ASSOC COMPUTING MACHINERY, Limassol, CYPRUS, pp. 307-317.View/Download from: Publisher's site
Zhou, J, Arshad, SZ, Luo, S & Chen, F 2017, 'Effects of Uncertainty and Cognitive Load on User Trust in Predictive Decision Making', HUMAN-COMPUTER INTERACTION - INTERACT 2017, PT IV, 16th IFIP TC 13 International Conference on Human-Computer Interaction (INTERACT), SPRINGER INTERNATIONAL PUBLISHING AG, Indian Inst Technol, Mumbai, INDIA, pp. 23-39.View/Download from: Publisher's site
Zhou, J, Arshad, SZ, Luo, S, Yu, K, Berkovsky, S & Chen, F 2017, 'Indexing cognitive load using blood volume pulse features', Conference on Human Factors in Computing Systems - Proceedings, pp. 2269-2275.View/Download from: Publisher's site
Copyright © 2017 by the Association for Computing Machinery, Inc. (ACM). Physiological responses contain rich affective information even when humans are not expressing any external signs. In this paper, we investigate the use of the Blood Volume Pulse (BVP) signals for indexing cognitive load. An experiment, which introduced cognitive load as a secondary task in a decision making context was conducted in the study. BVP signals were analyzed in order to establish relationships between BVP and cognitive load levels. A set of features (e.g. peak and max features) was found to be significantly distinctive across different cognitive load levels. The identified BVP features can be used to set up machine learning models for the automatic classification of CL levels in intelligent systems.
Chu, VW, Wong, RK, Chen, F & Chi, C-H 2017, 'Prediction-as-a-Service for Meme Popularity', 2017 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC), IEEE International Conference on Services Computing (SCC), IEEE, Honolulu, HI, pp. 386-393.View/Download from: Publisher's site
Do, Q, Liu, W & Chen, F 2017, 'Discovering both explicit and implicit similarities for cross-domain recommendation', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Jeju, South Korea, pp. 618-630.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer International Publishing AG. Recommender System has become one of the most important techniques for businesses today. Improving its performance requires a thorough understanding of latent similarities among users and items. This issue is addressable given recent abundance of datasets across domains. However, the question of how to utilize this cross-domain rich information to improve recommendation performance is still an open problem. In this paper, we propose a cross-domain recommender as the first algorithm utilizing both explicit and implicit similarities between datasets across sources for performance improvement. Validated on real-world datasets, our proposed idea outperforms the current cross-domain recommendation methods by more than 2 times. Yet, the more interesting observation is that both explicit and implicit similarities between datasets help to better suggest unknown information from cross-domain sources.
Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'DAAR: A discrimination-aware association rule classifier for decision support', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Conference on Big Data Analytics and Knowledge Discovery (DAWAK), Springer, Spain, pp. 47-68.View/Download from: UTS OPUS or Publisher's site
© Springer-Verlag GmbH Germany 2017. Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in data analytics. In this paper, we investigate how to build discrimination-aware models even when the available training set is intrinsically discriminating based on the sensitive attributes. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare DAAR with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. Our comprehensive evaluation is based on three measures: predictive accuracy, discrimination score and inclusion score. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination severity on all datasets with insignificant impact on the predictive accuracy. We also find that DAAR generates a small set of rules that are easy to understand and applied by users, to help them make discrimination-free decisions.
Anaissi, A, Khoa, NLD, Mustapha, S, Alamdari, MM, Braytee, A, Wang, Y & Chen, F 2017, 'Adaptive one-class support vector machine for damage detection in structural health monitoring', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Springer, Jeju, South Korea, pp. 42-57.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer International Publishing AG. Machine learning algorithms have been employed extensively in the area of structural health monitoring to compare new measurements with baselines to detect any structural change. One-class support vector machine (OCSVM) with Gaussian kernel function is a promising machine learning method which can learn only from one class data and then classify any new query samples. However, generalization performance of OCSVM is profoundly influenced by its Gaussian model parameter ϭ. This paper proposes a new algorithm named Appropriate Distance to the Enclosing Surface (ADES) for tuning the Gaussian model parameter. The semantic idea of this algorithm is based on inspecting the spatial locations of the edge and interior samples, and their distances to the enclosing surface of OCSVM. The algorithm selects the optimal value of ϭ which generates a hyperplane that is maximally distant from the interior samples but close to the edge samples. The sets of interior and edge samples are identified using a hard margin linear support vector machine. The algorithm was successfully validated using sensing data collected from the Sydney Harbour Bridge, in addition to five public datasets. The designed ADES algorithm is an appropriate choice to identify the optimal value of ϭ for OCSVM especially in high dimensional datasets.
Conway, D, Chen, F, Yu, K, Zhou, J & Morris, R 2016, 'Misplaced trust: A bias in human-machine trust attribution - In contradiction to learning theory', Conference on Human Factors in Computing Systems - Proceedings, pp. 3035-3041.View/Download from: Publisher's site
© 2016 Authors. Human-machine trust is a critical mitigating factor in many HCI instances. Lack of trust in a system can lead to system disuse whilst over-trust can lead to inappropriate use. Whilst human-machine trust has been examined extensively from within a technicosocial framework, few efforts have been made to link the dynamics of trust within a steady-state operatormachine environment to the existing literature of the psychology of learning. We set out to recreate a commonly reported learning phenomenon within a trust acquisition environment: Users learning which algorithms can and cannot be trusted to reduce traffic in a city. We failed to replicate (after repeated efforts) the learning phenomena of 'blocking', resulting in a finding that people consistently make a very specific error in trust assignment to cues in conditions of uncertainty. This error can be seen as a cognitive bias and has important implications for HCI.
Yu, K, Taib, R, Berkovsky, S, Zhou, J, Conway, D & Chen, F 2016, 'Trust and Reliance based on system accuracy', UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 223-227.View/Download from: Publisher's site
© 2016 ACM. Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system's output serves as one of the inputs for the users' decision making processes. In this work, we study the dynamics of explicit and implicit user trust in a simulated automated quality monitoring system, as a function of the system accuracy. We establish that users correctly perceive the accuracy of the system and adjust their trust accordingly.
Zhou, J, Arshad, SZ, Yu, K & Chen, F 2016, 'Correlation for User Confidence in Predictive Decision Making', PROCEEDINGS OF THE 28TH AUSTRALIAN COMPUTER-HUMAN INTERACTION CONFERENCE (OZCHI 2016), 28th Australian Computer-Human Interaction Conference (OzCHI), ASSOC COMPUTING MACHINERY, Univ Tasmania, Hobart, AUSTRALIA.View/Download from: Publisher's site
Zhou, J, Asif Khawaja, M, Li, Z, Sun, J, Wang, Y & Chen, F 2016, 'Making machine learning useable by revealing internal states update-a transparent approach', International Journal of Computational Science and Engineering, pp. 378-389.View/Download from: Publisher's site
© 2016 Inderscience Enterprises Ltd. Machine learning (ML) techniques are often found difficult to apply effectively in practice because of their complexities. Therefore, making ML useable is emerging as one of active research fields recently. Furthermore, an ML algorithm is still a 'black-box'. This 'black-box' approach makes it difficult for users to understand complicated ML models. As a result, the user is uncertain about the usefulness of ML results and this affects the effectiveness of ML methods. This paper focuses on making a 'black-box' ML process transparent by presenting real-time internal status update of the ML process to users explicitly. A user study was performed to investigate the impact of revealing internal status update to users on the easiness of understanding data analysis process, meaningfulness of real-time status update, and convincingness of ML results. The study showed that revealing of the internal states of ML process can help improve easiness of understanding the data analysis process, make real-time status update more meaningful, and make ML results more convincing.
Zhou, J, Li, Z, Zhang, Z, Liang, B & Chen, F 2016, 'Visual Analytics of Relations of Multi-Attributes in Big Infrastructure Data', 2016 INTERNATIONAL SYMPOSIUM ON BIG DATA VISUAL ANALYTICS (BDVA), International Symposium on Big Data Visual Analytics (BDVA), IEEE, Sydney, AUSTRALIA, pp. 31-32.
Chu, VW, Wong, RK, Chen, F & Chi, C-H 2016, 'Service Selection based on Dynamic QoS Networks', PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 13th IEEE International Conference on Services Computing (SCC), IEEE COMPUTER SOC, San Francisco, CA, pp. 98-105.View/Download from: Publisher's site
Chu, VW, Wong, RK, Chen, F, Fong, S & Hung, PCK 2015, 'Self-regularized causal structure discovery for trajectory-based networks', JOURNAL OF COMPUTER AND SYSTEM SCIENCES, 29th AAAI Conference on Artificial Intelligence (AAAI 2015), ACADEMIC PRESS INC ELSEVIER SCIENCE, Austin, TX, pp. 594-609.View/Download from: Publisher's site
Chu, VW, Wong, RK, Chen, F, Ho, I & Lee, J 2016, 'Enhancing Portfolio Return based on Market-sentiment Linked Topics', 2016 INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), International Conference on Big Data and Smart Computing (BigComp), IEEE, Hong Kong, PEOPLES R CHINA, pp. 85-92.
Fan, X, Li, B, Wang, Y, Wang, Y & Chen, F 2016, 'The ostomachion process', 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 1547-1553.
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Stochastic partition processes for exchangeable graphs produce axis-Aligned blocks on a product space. In relational modeling, the resulting blocks uncover the underlying interactions between two sets of entities of the relational data. Although some flexible axis-Aligned partition processes, such as the Mondrian process, have been able to capture complex interacting patterns in a hierarchical fashion, they are still in short of capturing dependence between dimensions. To overcome this limitation, we propose the Ostomachion process (OP), which relaxes the cutting direction by allowing for oblique cuts. The partitions generated by an OP are convex polygons that can capture inter-dimensional dependence. The OP also exhibits interesting properties: 1) Along the time line the cutting times can be characterized by a homogeneous Poisson process, and 2) on the partition space the areas of the resulting components comply with a Dirichlet distribution. We can thus control the expected number of cuts and the expected areas of components through hyper-parameters. We adapt the reversible-jump MCMC algorithm for inferring OP partition structures. The experimental results on relational modeling and decision tree classification have validated the merit of the OP.
Ghanavati, M, Wong, RK, Chen, F, Wang, Y & Fong, S 2016, 'A Generic Service Framework for Stock Market Prediction', PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 13th IEEE International Conference on Services Computing (SCC), IEEE COMPUTER SOC, San Francisco, CA, pp. 283-290.View/Download from: Publisher's site
Ghanavati, M, Wong, RK, Chen, F, Wang, Y & Fong, S 2016, 'Effective Local Metric Learning for Water Pipe Assessment', ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT I, 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), SPRINGER-VERLAG BERLIN, Univ Auckland, Auckland, NEW ZEALAND, pp. 565-577.View/Download from: Publisher's site
Ghanavati, M, Wong, RK, Chen, F, Wang, Y & Lee, J 2016, 'A hierarchical beta process approach for financial time series trend prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 227-237.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2016. An automatic stock market categorization system would be invaluable to investors and financial experts, providing them with the opportunity to predict a stock price changes with respect to the other stocks. In recent years, clustering all companies in the stock markets based on their similarities in shape of the stock market has increasingly become popular. However, existing approaches may not be practical because the stock price data are high-dimensional data and the changes in the stock price usually occur with shift, which makes the categorization more complex. In this paper, a hierarchical beta process (HBP) based approach is proposed for stock market trend prediction. Preliminary results show that the approach is promising and outperforms other popular approaches.
© 2016 NIPS Foundation - All Rights Reserved. The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e.g., hidden Markov model) and stochastic interaction point process models (e.g., Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing the hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and automatically determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Many natural and social phenomena can be modeled by interaction point processes (IPPs) (Diggle et al. 1994), stochastic point processes considering the interaction between points. In this paper, we propose the infinite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e.g., Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). It treats IPP as a mixture of basis point processes with the aid of a distance dependent prior over branching structure that describes the relationship between points. The IBM can estimate point event intensity, interaction mechanism and branching structure simultaneously. A generic Metropolis-within-Gibbs sampling method is also developed for model parameter inference. The experiments on synthetic and real-world data demonstrate the superiority of the IBM.
Luo, L, Li, B, Koprinska, I, Berkovsky, S & Chen, F 2016, 'Discovering Temporal Purchase Patterns with Different Responses to Promotions', CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 25th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, IUPUI, Indianapolis, IN, pp. 2197-2202.View/Download from: Publisher's site
ShafieiBavani, E, Ebrahimi, M, Wong, R & Chen, F 2016, 'A Query-based Summarization Service from Multiple News Sources', PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 13th IEEE International Conference on Services Computing (SCC), IEEE COMPUTER SOC, San Francisco, CA, pp. 42-49.View/Download from: Publisher's site
Yi, W, Li, B, Fan, X, Yang, W & Chen, F 2016, 'Bayesian optimization of partition layouts for mondrian processes', IJCAI International Joint Conference on Artificial Intelligence, pp. 2160-2166.
The Mondrian process (MP) produces hierarchical partitions on a product space as a kd-tree, which can be served as a flexible yet parsimonious partition prior for relational modeling. Due to the recursive generation of partitions and varying dimensionality of the partition state space, the inference procedure for the MP relational modeling is extremely difficult. The prevalent inference method reversible-jump MCMC for this problem requires a number of unnecessary retrospective steps to transit from one partition state to a very similar one and it is prone to fall into a local optimum. In this paper, we attempt to circumvent these drawbacks by proposing an alternative method for inferring the MP partition structure. Based on the observation that similar cutting rate measures on the partition space lead to similar partition layouts, we propose to impose a nonhomogeneous cutting rate measure on the partition space to control the layouts of the generated partitions - the original MCMC sampling problem is thus transformed into a Bayesian global optimization problem. The empirical tests demonstrate that Bayesian optimization is able to find better partition structures than MCMC sampling with the same number of partition structure proposals.
Nguyen, H, Liu, W, Rivera, P & Chen, F 2016, 'TrafficWatch: Real-time traffic incident detection and monitoring using social media', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, New Zealand, pp. 540-551.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2016. Social media has become a valuable source of real-time information. Transport Management Centre (TMC) in Australian state government of New South Wales has been collaborating with us to develop TrafficWatch, a system that leverages Twitter as a channel for transport network monitoring, incident and event managements. This system utilises advanced web technologies and state-of-the-art machine learning algorithms. The crawled tweets are first filtered to show incidents in Australia, and then divided into different groups by online clustering and classification algorithms. Findings from the use of TrafficWatch at TMC demonstrated that it has strong potential to report incidents earlier than other data sources, as well as identifying unreported incidents. TrafficWatch also shows its advantages in improving TMC's network monitoring capabilities to assess network impacts of incidents and events.
Cheema, P, Khoa, NLD, Alamdari, MM, Liu, W, Wang, Y, Chen, F & Runcie, P 2016, 'On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data', CIKM '16 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM International Conference on Information and Knowledge Management, ACM, Indianapolis, Indiana, USA, pp. 1813-1822.View/Download from: UTS OPUS or Publisher's site
Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.
Arshad, SZ, Zhou, J, Bridon, C, Chen, F & Wang, Y 2015, 'Investigating user confidence for uncertainty presentation in predictive decision making', OzCHI 2015: Being Human - Conference Proceedings, pp. 352-360.View/Download from: Publisher's site
Copyright © 2015 ACM. Machine Learning (ML) based decision support systems are often like a black box to non-expert users. Here user's confidence becomes critical for effective decision making and maintaining trust in the system. We find that user confidence varies significantly depending on supplementary material presented on screen. We investigate change in user confidence (in the context of ML based decision making) by varying level of uncertainty presented (in an online water-pipe failure prediction case study) and find that all 26 subjects rated higher uncertainty task to be most difficult and had lowest user confidence in predictive decisions of the same. This agrees with our expectation that increased uncertainty would reduce user confidence in predictive decision making. However, ML-researchers subgroup reported being most confident when uncertainty with known probability was presented, whereas other subgroups (viz. general staff and non-ML researchers) appeared most confident when uncertainty was not at all presented. This is an original research to improve understanding of user's decision making confidence with respect to uncertainty presented in machine learning context.
Chen, F, Marcus, N, Khawaji, A & Zhou, J 2015, 'Using galvanic skin response (GSR) to measure trust and cognitive load in the text-chat environment', Conference on Human Factors in Computing Systems - Proceedings, pp. 1989-1994.View/Download from: Publisher's site
Exchanging text messages via software on smart phones and computers has recently become one of the most popular ways for people to communicate and accomplish their tasks. However, there are negative aspects to using this kind of software, for example, it has been found that people communicating in the text-chat environment may experience a lack of trust and may face different levels of cognitive load [1, 11]. This study examines a novel way to measure interpersonal trust and cognitive load when they overlap with each other in the text-chat environment. We used Galvanic Skin Response (GSR), a physiological measurement, to collect data from twenty-eight subjects at four gradients and overlapping conditions between trust and cognitive load. The findings show that the GSR signals were significantly affected by both trust and cognitive load and provide promising evidence that GSR can be used as a tool for measuring interpersonal trust when cognitive load is low and also for measuring cognitive load when trust is high.
Oviatt, S, Hang, K, Zhou, J & Chen, F 2015, 'Spoken Interruptions Signal Productive Problem Solving and Domain Expertise in Mathematics', ICMI'15: PROCEEDINGS OF THE 2015 ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 2015 ACM International Conference on Multimodal Interaction, ASSOC COMPUTING MACHINERY, Seattle, WA, pp. 311-318.View/Download from: Publisher's site
Zhou, J, Bridon, C, Chen, F, Khawaji, A & Wang, Y 2015, 'Be informed and be involved: Effects of uncertainty and correlation on user's confidence in decision making', Conference on Human Factors in Computing Systems - Proceedings, pp. 923-928.View/Download from: Publisher's site
User's confidence in machine learning (ML) based decision making significantly affects acceptability of ML techniques. In this work, we investigate how uncertainty/correlation affects user's confidence in order to design effective user interface for ML-based intelligent systems. A user study was performed and we found that revealing of correlation helped users better understand uncertainty and thus increased confidence in model output. When correlation had the same trend with performance, correlation but not uncertainty helped users more confident in their decisions.
Zhou, J, Jung, JY & Chen, F 2015, 'Dynamic Workload Adjustments in Human-Machine Systems Based on GSR Features', HUMAN-COMPUTER INTERACTION, PT I, 15th IFIP TC.13 International Conference on Human-Computer Interaction (INTERACT), SPRINGER-VERLAG BERLIN, Bamberg, GERMANY, pp. 550-558.View/Download from: Publisher's site
Zhou, J, Sun, J, Chen, F, Wang, X & Miao, X 2015, 'Safety-Oriented Visual Analytics of People Movement', 2015 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY, 10th IEEE Conference on Visual Analytics Science and Technology (VAST), IEEE, Chicago, IL, pp. 181-182.
Arshad, S, Wang, Y & Chen, F 2015, 'Interactive mouse stream as real-time indicator of user's cognitive load', Conference on Human Factors in Computing Systems - Proceedings, pp. 1025-1030.View/Download from: Publisher's site
User interaction and multimodal behaviour have been argued as viable indicators of cognitive load. We extend this idea by exploring interactive mouse data stream and implementing sliding windows technique to detect cognitive load variation in real-time. This work-in-progress reports successful load change detections resulting from applying our unique algorithm to data streams of mouse interactivity features from twenty seven subjects. Unique contribution here includes learning from mouse interactive stream and a sliding window technique for cognitive load detection in real-time. This technique is currently being enhanced to process learning from multimodal user interaction streams. Copyright is held by the author/owner(s).
Chu, VW, Wong, RK, Chen, F & Chi, C-H 2015, 'Web Service Recommendations Based on Time-aware Bayesian Networks', 2015 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2015, IEEE International Congress on Big Data, IEEE, New York, NY, pp. 359-366.View/Download from: Publisher's site
Ghanavati, M, Wong, RK, Chen, F & Wang, Y 2015, 'A generic ranking service on scientific datasets', 2015 IEEE 12TH INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2015), 12th IEEE International Conference on Services Computing (SCC), IEEE, New York City, NY, pp. 491-498.View/Download from: Publisher's site
Hamzehei, A, Ebrahimi, M, ShafieiBavani, E, Wong, RK & Chen, F 2015, 'Scalable Sentiment Analysis for Microblogs based on Semantic Scoring', 2015 IEEE 12TH INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2015), 12th IEEE International Conference on Services Computing (SCC), IEEE, New York City, NY, pp. 271-278.View/Download from: Publisher's site
Lin, P, Zhang, B, Wang, Y, Li, Z, Li, B, Wang, Y & Chen, F 2015, 'Data driven water pipe failure prediction: A Bayesian nonparametric approach', International Conference on Information and Knowledge Management, Proceedings, pp. 193-202.View/Download from: Publisher's site
Water pipe failures can cause significant economic and social costs, hence have become the primary challenge to water utilities. In this paper, we propose a Bayesian nonparametric approach, namely the Dirichlet process mixture of hierarchical beta process model, for water pipe failure prediction. It can select high-risk pipes for physical condition assessment, thereby preventing disastrous failures proactively. The proposed method is adaptable to the diversity of failure patterns. Its model structure and complexity can automatically adjust according to observed data. Additionally, the sparse failure data problem that often occurs in real-world data is tackled by the proposed method via flexible pipe grouping and failure data sharing. An approximated yet computational efficient Metropolis-within-Gibbs sampling method is developed with the exploitation of the failure data sparsity for model parameter inference. The proposed method has been applied to a metropolitan water supply network. The details of the application context are also presented for demonstrating its real-life impact. The comparison experiments conducted on the metropolitan water pipe data show that the proposed approach significantly outperforms the state-of-the-art prediction methods, and it is capable of bringing enormous economic and social savings to water utilities.
Wang, Y, Li, B, Wang, Y & Chen, F 2015, 'Metadata dependent Mondrian processes', 32nd International Conference on Machine Learning, ICML 2015, pp. 1339-1347.
Copyright © 2015 by the author(s). Stochastic partition processes in a product space play an important role in modeling relational data. Recent studies on the Mondrian process have introduced more flexibility into the block structure in relational models. A side-effect of such high flexibility is that, in data sparsity scenarios, the model is prone to overfit. In reality, relational entities are always associated with meta information, such as user profiles in a social network. In this paper, we propose a metadata dependent Mondrian process (MDMP) to incorporate meta information into the stochastic partition process in the product space and the entity allocation process on the resulting block structure. MDMP can not only encourage homogeneous relational interactions within blocks but also discourage meta-label diversity within blocks. Regularized by meta information, MDMP becomes more robust in data sparsity scenarios and easier to converge in posterior inference. We apply MDMP to link prediction and rating prediction and demonstrate that MDMP is more effective than the baseline models in prediction accuracy with a more parsimonious model structure.
Khoa, NLD, Zhang, B, Wang, Y, Liu, W, Chen, F, Mustapha, S & Runcie, P 2015, 'On Damage Identification in Civil Structures Using Tensor Analysis', Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference Proceedings, Part 1, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Ho Chi Minh City, Vietnam, pp. 459-471.View/Download from: Publisher's site
Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'Discovering causal structures from time series data via enhanced granger causality', AI 2015: Advances in Artificial Intelligence (LNCS), Australasian Joint Conference on Artificial Intelligence, Springer, Canberra, Australia, pp. 365-378.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2015. Granger causality has been applied to explore predictive causal relations among multiple time series in various fields. However, the existence of nonstationary distributional changes among the time series variables poses significant challenges. By analyzing a real dataset, we observe that factors such as noise, distribution changes and shifts increase the complexity of the modelling, and large errors often occur when the underlying distribution shifts with time. Motivated by this challenge, we propose a new regression model for causal structure discovery – a Linear Model with Weighted Distribution Shift (linear WDS), which improves the prediction accuracy of the Granger causality model by taking into account the weights of the distribution-shift samples and by optimizing a quadratic-mean based objective function. The linear WDS is integrated in the Granger causality model to improve the inference of the predictive causal structure. The performance of the enhanced Granger causality model is evaluated on synthetic datasets and real traffic datasets, and the proposed model is compared with three different regression-based Granger causality models (standard linear regression, robust regression and quadratic-mean-based regression). The results show that the enhanced Granger causality model outperforms the other models especially when there are distribution shifts in the data.
Luo, L, Liu, W, Koprinska, I & Chen, F 2015, 'Discrimination-aware association rule mining for unbiased data analytics', Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings, International Conference on Big Data Analytics and Knowledge Discovery, Springer International Publishing, Valencia; Spain, pp. 108-120.View/Download from: Publisher's site
A discriminatory dataset refers to a dataset with undesirable correlation between sensitive attributes and the class label, which often leads to biased decision making in data analytics processes. This paper investigates how to build discrimination-aware models even when the available training set is intrinsically discriminating based on some sensitive attributes, such as race, gender or personal status. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare it with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination on all datasets with insignificant impact on the predictive accuracy.
Khawaji, A, Chen, F, Zhou, J & Marcus, N 2014, 'Trust and cognitive load in the text-chat environment: The role of mouse movement', Proceedings of the 26th Australian Computer-Human Interaction Conference, OzCHI 2014, pp. 324-327.
Copyright 2014 ACM. This paper examines how different levels of cognitive load can affect trust in the text-chat environment. It also examines how the mouse movements of participants can indicate the level of cognitive load when they chat with each other. We designed two chat systems: one in which subjects chat under low mental load and the other in which subjects chat under high mental load. Twenty subjects participated in the study and the results showed significant differences in the level of trust between subjects under different cognitive loads; that is, subjects who chatted under low mental load showed more trust in their partners. Moreover, the mouse data obtained proved to be effective in indicating the level of cognitive load existing between the subjects. However, this work suggests that to establish trust in the chat environment, it is better to communicate under a low cognitive load. Our findings also show the ability of designed systems to measure cognitive load via tracking mouse events for the purpose of providing assistance to communicators.
Zhou, J, Hang, K, Oviatt, S, Yu, K & Chen, F 2014, 'Combining empirical and machine learning techniques to predict math expertise using pen signal features', MLA 2014 - Proceedings of the 2014 ACM Multimodal Learning Analytics Workshop and Grand Challenge, Co-located with ICMI 2014, pp. 29-36.View/Download from: Publisher's site
© 2014 ACM. Multimodal learning analytics aims to automatically analyze students' natural communication patterns based on speech, writing, and other modalities during learning activities. This research used the Math Data Corpus, which contains timesynchronized multimodal data from collaborating students as they jointly solved problems varying in difficulty. The aim was to investigate how reliably pen signal features, which were extracted as students wrote with digital pens and paper, could identify which student in a group was the dominant domain expert. An additional aim was to improve prediction of expertise based on joint bootstrapping of empirical science and machine learning techniques. To accomplish this, empirical analyses first identified which data partitioning and pen signal features were most reliably associated with expertise. Then alternative machine learning techniques compared classification accuracies based on all pen features, versus empirically selected ones. The best unguided classification accuracy was 70.8%, which improved to 83.3% with empirical guidance. These results demonstrate that handwriting signal features can predict domain expertise in math with high reliability. Hybrid methods also can outperform blackbox machine learning in both accuracy and transparency.
Chu, VW, Wong, RK, Liu, W & Chen, F 2014, 'Causal Structure Discovery for Spatio-temporal Data', DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2014, PT I, 19th International Conference on Database Systems for Advanced Applications (DASFAA), SPRINGER-VERLAG BERLIN, Bali, INDONESIA, pp. 236-250.
Chu, VW, Wong, RK, Liu, W, Chen, F & Perng, C-S 2014, 'Traffic Analysis as a Service via a Unified Model', 2014 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2014), 11th IEEE International Conference on Services Computing (SCC), IEEE, Anchorage, AK, pp. 195-202.View/Download from: Publisher's site
Ghanavati, M, Wong, RK, Chen, F, Wang, Y & Perng, C-S 2014, 'An Effective Integrated Method for Learning Big Imbalanced Data', 2014 IEEE INTERNATIONAL CONGRESS ON BIG DATA (BIGDATA CONGRESS), 3rd IEEE International Congress on Big Data, IEEE, Anchorage, AK, pp. 691-698.View/Download from: Publisher's site
Zhang, B, Wang, Y, Wang, Y & Chen, F 2014, 'Stable learning in coding space for multi-class decoding and its extension for multi-class hypothesis transfer learning', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1075-1081.View/Download from: Publisher's site
© 2014 IEEE. Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding. Most of these approaches focus on the first two phases and predefined distance function is used for decoding. In this paper, however, we propose to perform learning in coding space for more adaptive decoding, thereby improving overall performance. Ramp loss is exploited for measuring multi-class decoding error. The proposed algorithm has uniform stability. It is insensitive to data noises and scalable with large scale datasets. Generalization error bound and numerical results are given with promising outcomes.
Liu, W, Sarda, A, Chen, F & Geers, G 2014, 'Forecasting changes of traffic flow caused by road incidents', 21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World.
This paper explores the potential for supervised machine learning techniques in forecasting changes of traffic flow caused by road incidents based on incident features. Data fusion approaches are carried out on a high quality SCATS dataset measuring traffic flow of a major Australian city, and on an incident log data set encompassing a time period of 4 months' road incidents. Based on incident features, a range of both prevalent and advanced machine learning algorithms are applied to these data, and the accuracies of the algorithms are evaluated. We then examine the effectiveness of such models in categorizing changes of traffic flow as either trivial or non-trivial in the extent of their responses to incidents. The models are promising in their capacity and are able to correctly predict with more than 70% accuracy that a change of traffic flow shall be major. This has significant implications for determining the optimal allocation of resources for both road traffic control and incident response units.
Khawaji, A, Chen, F, Marcus, N & Zhou, J 2013, 'Trust and cooperation in text-based computer-mediated communication', Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, OzCHI 2013, pp. 37-40.View/Download from: Publisher's site
This study examines how different behaviours can affect trust in the text-chat environment. We designed two automated chat systems: one behaves cooperatively and the other behaves competitively. Thirty subjects participated in this study and the results revealed that the trust of subjects who chatted with a cooperative partner was significantly higher than the trust of subjects who chatted with a competitive partner. This study also examines the chat contents and the results show that subjects behave differently when they trust their partner, using more assent and positive emotion words.
Yu, K, Epps, J & Chen, F 2013, 'Mental Workload Classification via Online Writing Features', 2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR), 12th International Conference on Document Analysis and Recognition (ICDAR), IEEE, Washington, DC, pp. 1110-1114.View/Download from: Publisher's site
Conway, D, Dick, I, Li, Z, Wang, Y & Chen, F 2013, 'The Effect of Stress on Cognitive Load Measurement', HUMAN-COMPUTER INTERACTION - INTERACT 2013, PT IV, 14th IFIP TC 13 INTERACT International Conference on Designing for Diversity, SPRINGER-VERLAG BERLIN, Cape Town, SOUTH AFRICA, pp. 659-666.
Nourbakhsh, N, Wang, Y & Chen, F 2013, 'GSR and Blink Features for Cognitive Load Classification', HUMAN-COMPUTER INTERACTION - INTERACT 2013, PT I, 14th IFIP TC 13 INTERACT International Conference on Designing for Diversity, SPRINGER-VERLAG BERLIN, Cape Town, SOUTH AFRICA, pp. 159-166.
Wang, W, Li, Z, Wang, Y & Chen, F 2013, 'Indexing cognitive workload based on pupillary response under luminance and emotional changes', International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 247-256.View/Download from: Publisher's site
Pupillary response is a popular physiological index of cognitive workload that can be used for design and evaluation of adaptive interface in various areas of human-computer interaction (HCI) research. However, in practice various confounding factors unrelated to workload, including changes of luminance condition and emotional arousal might degrade pupillary response based workload measures such as commonly used mean pupil diameter. This work investigates pupillary response as a cognitive workload measure under the influence of such confounding factors. Video-based eye tracker is used to record pupillary response during arithmetic tasks under luminance and emotional changes. Machine learning based feature selection and classification techniques are proposed to robustly index cognitive workload based on pupillary response even with the influence of noisy factors unrelated to workload. Copyright © 2013 ACM.
Wang, W, Wang, Y, Chen, F & Sowmya, A 2013, 'A weakly supervised approach for object detection based on Soft-Label Boosting', Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 331-338.View/Download from: Publisher's site
Object detection is an important and challenging problem in the field of computer vision. Classical object detection approaches such as background subtraction and saliency detection do not require manual collection of training samples, but can be easily affected by noise factors, such as luminance changes and cluttered background. On the other hand, supervised learning based approaches such as Boosting and SVM usually have robust performance, but require substantial human effort to collect and label training samples. This study aims to combine the comparative advantages of both kinds of approaches, and its contributions are two-fold: (i) a weakly supervised approach for object detection, which does not require manual collection and labelling of training samples; (ii) an extension of Boosting algorithm denoted as Soft-Label Boosting, which is able to employ training samples with soft (probabilistic) labels instead of hard (binary) labels. Experimental results show that the proposed weakly supervised approach outperforms the state-of-the-art, and even achieves comparable performance to supervised approaches. © 2013 IEEE.
Wang, Y, Li, Z, Wang, Y & Chen, F 2013, 'A Bayesian Non-parametric Viewpoint to Visual Tracking', 2013 IEEE WORKSHOP ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE Workshop on Applications of Computer Vision (WACV), IEEE, Clearwater, FL, pp. 482-488.
Yu, K, Epps, J & Chen, F 2011, 'Cognitive load evaluation of handwriting using stroke-level features', International Conference on Intelligent User Interfaces, Proceedings IUI, pp. 423-426.View/Download from: Publisher's site
This paper examines several writing features for the evaluation of cognitive load. Our analysis is focused on writing features within and between written strokes, including writing pressure, writing velocity, stroke length and inter-stroke movements. Based on a study of 20 subjects performing a sentence composition task, the reported findings reveal that writing pressure and writing velocity information are very good indicators of cognitive load. A stroke selection threshold was investigated for constraining the feature extraction to long strokes, which resulted in a small further improvement. Differing from most previous research investigating cognitive load during writing based on task performance criteria, this work proposes a new approach to cognitive load measurement using writing dynamics, with the potential to allow new or improve existing handwriting interfaces. © 2011 ACM.
Knoll, A, Wang, Y, Chen, F, Xu, J, Ruiz, N, Epps, J & Zarjam, P 2011, 'Measuring cognitive workload with low-cost electroencephalograph', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 568-571.View/Download from: Publisher's site
Electroencephalography (EEG) is an important physiological index of cognitive workload. While previous research has employed high-end EEG devices, this work investigates the feasibility of measuring cognitive workload with a low-cost EEG system. In our experiment, EEG signals are recorded from subjects performing silent reading tasks under different difficulty levels. Experimental results demonstrate the effectiveness of cognitive workload evaluation even with low-cost EEG equipment. © 2011 IFIP International Federation for Information Processing.
Xu, J, Wang, Y, Chen, F & Choi, E 2011, 'Pupillary response based cognitive workload measurement under luminance changes', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 178-185.View/Download from: Publisher's site
Pupillary response has been widely accepted as a physiological index of cognitive workload. It can be reliably measured with remote eye trackers in a non-intrusive way. However, pupillometric measurement might fail to assess cognitive workload due to the variation of luminance conditions. To overcome this problem, we study the characteristics of pupillary responses at different stages of cognitive process when performing arithmetic tasks, and propose a fine-grained approach for cognitive workload measurement. Experimental results show that cognitive workload could be effectively measured even under luminance changes. © 2011 IFIP International Federation for Information Processing.
Zarjam, P, Epps, J & Chen, F 2011, 'CHARACTERIZING WORKING MEMORY LOAD USING EEG DELTA ACTIVITY', 19TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO-2011), 19th European Signal Processing Conference (EUSIPCO), EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP, Barcelona, SPAIN, pp. 1554-1558.
Zarjam, P, Epps, J & Chen, F 2011, 'Spectral EEG Featuresfor Evaluating Cognitive Load', 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS), IEEE, Boston, MA, pp. 3841-3844.
Khawaja, MA, Chen, F & Marcus, N 2010, 'Using Language Complexity to Measure Cognitive Load for Adaptive Interaction Design', IUI 2010, Proceedings of the 14th ACM International Conference on Intelligent User Interfaces, ASSOC COMPUTING MACHINERY, Hong Kong, PEOPLES R CHINA, pp. 333-336.
Sun, Y, Shi, YD, Chen, F & Chung, V 2009, 'Building a Practical Multimodal System with a Multimodal Fusion Module', HUMAN-COMPUTER INTERACTION, PT II, 13th International Conference on Human-Computer Interaction, SPRINGER-VERLAG BERLIN, San Diego, CA, pp. 93-+.
Yap, TF, Ambikairajah, E, Choi, E & Chen, F 2009, 'PHASE BASED FEATURES FOR COGNITIVE LOAD MEASUREMENT SYSTEM', 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Taipei, TAIWAN, pp. 4825-+.
Yin, B, Ambikairajah, E & Chen, F 2009, 'VOICED/UNVOICED PATTERN-BASED DURATION MODELING FOR LANGUAGE IDENTIFICATION', 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Taipei, TAIWAN, pp. 4341-4344.View/Download from: Publisher's site
Yin, B, Ambikairajah, E & Chen, F 2008, 'Improvements on Hierarchical Language Identification based on automatic language clustering', 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 33rd IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Las Vegas, NV, pp. 4241-4244.
Yin, B, Chen, F, Ruiz, N & Ambikairajah, E 2008, 'Speech-based cognitive load monitoring system', 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 33rd IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Las Vegas, NV, pp. 2041-2044.View/Download from: Publisher's site
Yin, B, Thiruvaran, T, Ambikairajah, E & Chen, F 2008, 'Introducing a FM based Feature to Hierarchical Language Identification', INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5, 9th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2008), ISCA-INT SPEECH COMMUNICATION ASSOC, Brisbane, AUSTRALIA, pp. 731-734.
Chen, F, Choi, EHC & Wang, N 2007, 'Exploiting speech-gesture correlation in multimodal interaction', HUMAN-COMPUTER INTERACTION, PT 3, PROCEEDINGS, 12th International Conference on Human-Computer Interaction (HCI International 2007), SPRINGER-VERLAG BERLIN, Beijing, PEOPLES R CHINA, pp. 23-+.
Ruiz, N, Taib, R, Shi, Y, Choi, E & Chen, F 2007, 'Using Pen Input Features as Indices of Cognitive Load', ICMI'07: PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON MULTIMODAL INTERFACES, 9th International Conference on Multimodal Interfaces, ASSOC COMPUTING MACHINERY, Nagoya, JAPAN, pp. 315-+.
Sun, Y, Shi, Y, Chen, F & Chung, V 2007, 'An efficient Multimodal language processor for parallel input strings in multimodal input fusion', ICSC 2007: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, PROCEEDINGS, International Conference on Semantic Computing (ICSC 2007), IEEE COMPUTER SOC, Irvine, CA, pp. 389-+.View/Download from: Publisher's site
Yin, B & Chen, F 2007, 'Towards automatic cognitive load measurement from speech analysis', HUMAN-COMPUTER INTERACTION, PT 1, PROCEEDINGS, 12th International Conference on Human-Computer Interaction (HCI International 2007), SPRINGER-VERLAG BERLIN, Beijing, PEOPLES R CHINA, pp. 1011-+.
Chen, F, Close, B, Eades, P, Epps, J, Hutterer, P, Lichman, S, Takatsuka, M, Thomas, B & Wu, M 2006, 'ViCAT: Visualisation and interaction on a collaborative access table', FIRST IEEE INTERNATIONAL WORKSHOP ON HORIZONTAL INTERACTIVE HUMAN-COMPUTER SYSTEMS, 1st IEEE International Workshop on Horizontal Interactive Human-Computer Systems, IEEE COMPUTER SOC, Adelaide, AUSTRALIA, pp. 59-+.
Yin, B, Ambikairajah, E & Chen, F 2006, 'Combining cepstral and prosodic features in language identification', 18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS, 18th International Conference on Pattern Recognition (ICPR 2006), IEEE COMPUTER SOC, Hong Kong, PEOPLES R CHINA, pp. 254-+.